Characterising droughts in Central America with uncertain hydro-meteorological data

Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS)

Influence of mathematical and physical background of drought indices on their complementarity and drought recognition ability

Due to the inherent complexity of drought phenomena, difference in hydro-climatic conditions and watershed characteristics, there is a lack of a universal drought index for assessing drought conditions in a particular region. Hence, the performance evaluation of different drought indices is necessary for identifying a suitable drought index. In this study, the performance of five drought indices was assessed for a semi-arid basin located in western India. The data from nine raingauge stations and four climate stations for the period of 25 years (1985-2009) were used. Based on the data availability, five meteorological drought indices were selected, viz., Percent Departure from Normal (PDN), Effective Drought Index (EDI), Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Standardized Precipitation Evapotranspiration Index (SPEI). Among these drought indices, EDI is a time step independent drought index and other four multi-time scale indices were defined at 1-, 3-, 6-, 9- and 12-month scales. The scale for the comparison of these drought indices was chosen based on the correlation with EDI and within among the scales of multi-time scale drought indices. The performance of drought indices during the historical drought was evaluated based on the relative frequency of drought index in a particular drought severity class and the response of these indices with multi-monthly rainfalls. In addition, the performance of these drought indices was assessed using the decision criteria such as Robustness, Tractability, Transparency, Sophistication, and Extendability. Analysis of the results indicated that the 9-month scale is appropriate for comparing drought indices in the study area. SPEI-9 showed maximum relative frequency in the ‘severe dry’ class and was found sensitive to 9-monthly rainfall at most of the stations. Further, the results of the performance evaluation criteria revealed that SPEI-9 has the highest total weighted score (136) followed by RDI-9, SPI-9, EDI, and PDN-9. It is concluded that SPEI-9 is the most suitable drought index for monitoring drought conditions in the study area.

Comparability analyses were carried out to investigate behavioural aspects of effective drought index (EDI), standardized precipitation index (SPI), reconnaissance drought index (RDI) and standardized precipitation evapotranspiration index (SPEI), considering 3-month, 6-month and annual time periods. Investigations included parametric/non-parametric correlation analysis among indices, climatic zone influence, record length impacts and evapotranspiration role (RDI and SPEI) on Markov chains predictability characteristics. Except for the EDI, all indices/cases (all climatic zones) showed significant correlation. In arid/semi-arid climates, the 3-month and 6-month maximum drought severities were detected by the RDI and annual maximum drought severities were detected by the SPEI, emphasizing the evapotranspiration influence. In all climatic zones, the EDI values for wet (dry) periods were higher (lower), compared to other indices. First order dependency was detected for the EDI (all cases) and the SPI (most cases), over entire period (1951–2011) and sub-periods [(1951–1981), (1982–2011)]. The largest number of second order dependency was detected by the SPEI, followed by a relatively large number of such cases by the RDI (3-month time period), for the 61-year data period. This research showed that several factors influence Markov chains predictability characteristics in drought studies, particularly the impact of record length and evapotranspiration (RDI and SPEI) were confirmed.

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.

Water is increasingly becoming scarce resource in many parts of the world. Precipitation deficiency due to natural climatic variability in space and time is the primary cause of drought. Drought is a complex phenomenon that is difficult to accurately describe. The most common tool used to monitor drought is a drought index. Drought characterization is one of the important aspects in crop planning and resource development in rainfed farming areas. In this study, monthly rainfall at nine tehasils in Amravati district of Maharashtra was analyzed to estimate and compare decile drought index, effective drought index (EDI) and standardized precipitation index (SPI) for identifying drought years using 22 years rainfall data (1991–2012). Decile drought index identified maximum number of years in moderate drought category and widespread drought during 11 years. Effective drought index identified maximum number of years in normal condition and only one year 1995 as widespread drought year. SPI identified maximum number of years in mild wet condition and widespread drought was observed in four years in Amravati district. The three indices were compared using seven assessment criterion adopted by Ntale and Gan (2003). The SPI was found to be superior over that of decile index and EDI because it describes all the major droughts occurred in Amravati district, it has higher positive values of Spearman rank correlation coefficient between year wise crop yield data and climatic severity, it shows more consistency with historical drought events, it is easily adapted to the local climate, it can be computed at almost any time scale, it has no theoretical upper or lower bounds and it fulfills the criteria of data requirement and availability for its assessment. SPI can be considered as the most suitable index for drought assessment in Amravati district.

Drought has threatened sustainability in terrestrial ecosystems. Although a series of indices have been proposed, they have largely overlooked the performance in characterizing regional or local drought, which is significant in acknowledging the impacts of drought on regional carbon cycling. To extract site-specific optimal drought index for vegetation in China, vegetation net primary production (NPP) estimated by Terrestrial Ecosystem Carbon Flux model, was compared with 11 worldwide-used indices, through sensitivity analysis and Generalizes Estimation Equation method. It was found that NPP in northern areas were significantly sensitive and correlated to 7 ~ 11 drought indices, whereas southern areas were sensitive to 1 ~ 4 indices. Among the drought indices, moisture index was highlighted as the best one, followed by standardized precipitation index, effective drought index, drought reconnaissance index, and standardized precipitation evapotranspiration index. Using optimal drought indices for local applications, the dynamics of drought and its impact on vegetation were quantified from 2000 to 2022. A general increase in vegetation NPP and decrease in drought were demonstrated in major northern and eastern areas. Moderately-extremely drought led to > 20% and even > 40% loss in NPP across northern and northwestern areas, much larger than the loss in southern areas. Moreover, forests were exposed to a mean loss of −20.1% and standard deviation of 13.2% in NPP, smaller than croplands and grasslands. These results contribute to a better understanding of drought dynamic and coupling of vegetation production, and guide for planning mitigations in drought-prone hotpots to ensure region's resilience.

This study examines the (dis)similarity of two commonly used indices Standardized Precipitation Index (SPI) computed over accumulation periods 1-month, 3-month, 6-month, and 12-month (hereafter SPI-1, SPI-3, SPI-6, and SPI-12, respectively) and Effective Drought Index (EDI). The analysis is based on two drought monitoring indicators (derived from SPI and EDI), namely, the Drought Duration (DD) and Drought Severity (DS) across the 93 South African Weather Service’s delineated rainfall districts over South Africa from 1980 to 2019. In the study, the Pearson correlation coefficient dissimilarity and periodogram dissimilarity estimates were used. The results indicate a positive correlation for the Pearson correlation coefficient dissimilarity and a positive value for periodogram of dissimilarity in both the DD and DS. With the Pearson correlation coefficient dissimilarity, the study demonstrates that the values of the SPI-1/EDI pair and the SPI-3/EDI pair exhibit the highest similar values for DD, while the SPI-6/EDI pair shows the highest similar values for DS. Moreover, dissimilarities are more obvious in SPI-12/EDI pair for DD and DS. When a periodogram of dissimilarity is used, the values of the SPI-1/EDI pair and SPI-6/EDI pair exhibit the highest similar values for DD, while SPI-1/EDI displayed the highest similar values for DS. Overall, the two measures show that the highest similarity is obtained in the SPI-1/EDI pair for DS. The results obtainable in this study contribute towards an in-depth knowledge of deviation between the EDI and SPI values for South Africa, depicting that these two drought indices values are replaceable in some rainfall districts of South Africa for drought monitoring and prediction, and this is a step towards the selection of the appropriate drought indices.

Although many drought indices have been developed to monitor drought conditions, their applicability differs across climatic regions, which results in different characteristics of drought index assessments at different timescales and over different record lengths. Therefore, the applicability of precipitation-based and precipitation-evapotranspiration/temperature-based drought indices was examined in this study using the Generalized Extreme Value Index (GEVI), Homogeneity Index (HI) of precipitation and temperature, K index (K), precipitation anomaly percentage (Pa), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and China-Z Index (CZI) over different record lengths and at a timescale range of 1-24months. In addition, the results that were obtained using these indices were compared with the Self-Calibrating Palmer Drought Severity Index (scPDSI). The stability, accuracy, and consistency of the different drought indices were evaluated using precipitation and evapotranspiration/temperature data (1-24months) collected from different climatic regions in the arid area of northwest China in the 1961-2017 period. The results indicated that the Pa, CZI, K, and SPEI were more stable; the SPI, SPEI, and HI were more accurate; and the GEVI, SPI, HI, and SPEI were more consistent with the scPDSI. In addition, the results indicated that it is more appropriate to select a long record length (> 35 years) to monitor drought when sufficient data are available. However, defining constant drought classes may not be appropriate for all drought timescales. In fact, precipitation and evapotranspiration data from different timescales had different optimal distribution functions. The drought indices also demonstrated that they were applicable to a temperate arid area in the study region. In addition, the HI and SPEI better captured the precipitation and evapotranspiration/temperature characteristics, while the CZI, K, and Pa overestimated or underestimated the frequency of different drought classes at different timescales to some extent in the study region. The results of this study suggest that greater priority should be given to the precipitation-evapotranspiration-based indices. In addition, it is suggested to change the drought index class thresholds in future related studies on drought event recognition at different timescales to ensure more accurate drought monitoring.

Abstract. Distinction between drought and aridity is crucial to understand water scarcity processes. Drought indices are used for drought identification and drought severity characterisation. The Standardised Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI) are the most known drought indices. In this study, they are compared with the modified PDSI for Mediterranean conditions (MedPDSI) and the Standardised Precipitation Evapotranspiration Index (SPEI). MedPDSI results from the soil water balance of an olive crop, thus real evapotranspiration is considered, while SPEI uses potential (climatic) evapotranspiration. Similarly to the SPI, SPEI can be computed at various time scales. Aiming at understanding possible impacts of climate change, prior to compare the drought indices, a trend analysis relative to precipitation and temperature in 27 weather stations of Portugal was performed for the period 1941 to 2006. A trend for temperature increase was observed for some weather stations and trends for decreasing precipitation in March and increasing in October were also observed for some locations. Comparisons of the SPI and SPEI at 9- and 12-month time scales, the PDSI and MedPDSI were performed for the same stations and period. SPI and SPEI produce similar results for the same time scales concerning drought occurrence and severity. PDSI and MedPDSI correlate well between them and the same happened for SPI and SPEI. PDSI and MedPDSI identify more severe droughts than SPI or SPEI and identify drought occurrence earlier than these indices. This behaviour is likely to be related with the fact that a water balance is performed with PDSI and MedPDSI, which better approaches the supply-demand balance.

Projecting drought characteristics over East African basins at specific global warming levels

Drought evolution, severity and trends in mainland China over 1961–2013

Drought forecasting is a critical component of drought risk management. The paper describes an approach to drought forecasting, which makes use of Artificial Neural Network (ANN) and predicts quantitative values of drought indices—continuous functions of rainfall which measure the degree of dryness of any time period. The indices used are the Effective Drought Index (EDI) and the Standard Precipitation Index (SPI). The forecasts are attempted using different combinations of past rainfall, the above two drought indices in preceding months and climate indices like Southern Oscillation Index (SOI) and North Atlantic Oscillation (NAO) index. A number of different ANN models for both EDI and SPI with the lead times of 1 to 12 months have been tested at several rainfall stations in the Tehran Province of Iran. The best models in both cases have been found to include, among the others, a corresponding drought index value from the same month of the previous year. Both best models have the R2 values of 0.66‐0.79 for a lead time of 6 months, but it is also shown that the EDI forecasts are superior to those of the SPI for all lead times and at all rainfall stations. The better performance of the EDI model is illustrated by its more accurate prediction of the overall pattern of ‘dry’ and ‘wet’ conditions. The structure of the model inputs (previous rain and drought indices) does not vary with the lead time, which makes the models very convenient for the operational purposes. The final forecasting models can be utilized by drought early warning systems, which are emerging in Iran at present. Copyright © 2007 Royal Meteorological Society

Drought is a common natural disaster worldwide, with varying durations, severity levels, and spatial extents. This study aimed to model the spatiotemporal variation of meteorological drought events in the Gabiley region of Somaliland. The study utilized primary data collected from the meteorological station in Gabiley and CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data to develop the standardized precipitation index (SPI) at a 3-month timescale. The results of the study revealed that the study area was characterized by drought and received inadequate precipitation, resulting in catastrophic droughts that negatively impacted the socioeconomic situation of the community. Mild-to-severe meteorological drought events occurred every two to three years, with the most severe droughts occurring in 1998, 2002, 2009, 2015, and 2017. Specifically, the year 2015 experienced the most severe meteorological drought in the region during the studied period. The predominant type of drought was a mild year in the study area. The SPI was found to potentially identify meteorological drought, making it a useful tool for policymakers as they develop drought adaptation and mitigation policies. This study provides valuable information that can benefit local authorities and policymakers in creating drought mitigation and adaptation strategies in the Gabiley region.

A novel intelligent approach for predicting meteorological drought based on satellite-based precipitation product: Application of an EMD-DFA-DBN hybrid model